How much VRAM do you need to run FP4 video models in ComfyUI on an RTX GPU?

The Short Answer: FP4 Cuts Video Model VRAM Roughly in Half

If you want the direct answer up front: running FP4-quantized video generation models in ComfyUI typically requires somewhere between 8 GB and 16 GB of VRAM for the model weights themselves, compared to 16 GB to 24 GB or more for the same models at FP16/BF16 precision. NVIDIA's RTX-accelerated ComfyUI integration, announced through 2025 and expanded into 2026, brought native FP4 support to GeForce RTX GPUs — particularly the RTX 50-series Blackwell cards — so that video diffusion models like WAN and other text-to-video checkpoints can generate locally without a workstation-class GPU. For practical purposes, an RTX 4070 Ti Super (16 GB) or any RTX 50-series card with 12–16 GB can run FP4 video models comfortably at 720p, while 8 GB cards are workable but tight once you account for VAE decoding and latent buffers.

Also worth reading: How Do I Use an RTX 5090 for ComfyUI 4K Video Upscaling? · How can creators implement the LTX 2.5 ComfyUI optimization guide to achieve native 4K video generation on RTX hardware? · How Do Professionals Approach Optimizing ComfyUI for High-Resolution Video Production?

The reason this matters for anyone interested in AI video upscaling is straightforward: generation and upscaling compete for the same VRAM. If your generation pipeline eats 14 GB, you have little headroom left to load an upscaling model like SeedVR2, Topaz-style enhancement nodes, or RTX Video Super Resolution passes in the same workflow. FP4 quantization is what makes a single-GPU generate-then-upscale-to-4K workflow realistic on consumer hardware.

What FP4 Actually Is and Why It Reduces Memory So Dramatically

FP4 is a 4-bit floating-point format introduced with NVIDIA's Blackwell architecture. Where FP16 stores each weight using 16 bits, FP4 uses only 4 bits — a 4x reduction in raw storage per parameter. In practice, real-world savings land closer to 50–70% versus FP16 once you factor in activation memory, KV-style caches for temporal attention, and the overhead of quantization scales, because not every tensor in a diffusion transformer benefits equally from 4-bit compression.

The trade-off is precision. Four-bit floats have very limited dynamic range and mantissa resolution, so naive FP4 conversion degrades output quality — flickering frames, muddier textures, and weaker prompt adherence are common failure modes. NVIDIA's implementation addresses this by keeping sensitive components (often the final layers, embeddings, or normalization) at higher precision while aggressively quantizing the bulk of the transformer blocks. This mixed-precision approach is why FP4 video models in ComfyUI look noticeably better than early community 4-bit experiments that simply truncated weights.

It's also worth being honest about the limits: FP4 is not free quality-wise even when done well. Side-by-side comparisons against BF16 checkpoints usually show subtle differences in fine motion detail and texture consistency. Whether that matters depends on your end goal — if the output is going through a 4K upscaling pass anyway, small generative artifacts often get smoothed or, occasionally, amplified. Test both before committing to a pipeline.

Hardware Requirements: Which RTX Cards Can Run FP4 Video Models

FP4 acceleration in ComfyUI is tied to NVIDIA's TensorRT and Blackwell-era hardware support. The RTX 5090 (32 GB), 5080 (16 GB), 5070 Ti (16 GB), and 5070 (12 GB) all handle FP4 natively and see the largest speedups. Ada-generation cards like the RTX 4090 and 4080 also benefit substantially because TensorRT supports FP8/INT4 paths there, though peak FP4 throughput is highest on Blackwell. Older Turing and Ampere cards can still run quantized models via GGUF-style weight loading in ComfyUI, but they miss out on the native FP4 kernel acceleration that makes the NVIDIA integration fast.

The pattern here is clear: 16 GB is the practical sweet spot for a combined generate-and-upscale workflow, while 8 GB cards force compromises such as sequential loading, CPU offloading, or lower-resolution generation followed by aggressive upscaling. System RAM matters too — ComfyUI's smart offloading means 32–64 GB of system memory lets an 8 GB GPU punch above its weight by swapping model components in and out, at a meaningful speed penalty.

How the ComfyUI + NVIDIA Integration Works in Practice

NVIDIA worked directly with the ComfyUI team to ship RTX-accelerated nodes and templates, so the setup path is much simpler than it was in 2024 when you had to hand-build TensorRT engines. The current flow looks like this: install or update ComfyUI to a recent build, install the NVIDIA-provided custom node pack, download an FP4-quantized checkpoint of your chosen video model, and select the RTX template from the workflow browser. The templates handle the quantization-aware loading automatically rather than requiring you to convert weights yourself.

Under the hood, TensorRT compiles optimized kernels for your specific GPU, which is where most of the speedup comes from — reports around the integration launch cited multi-fold improvements in video generation speed versus unoptimized PyTorch execution on the same card. Alongside the FP4 work, NVIDIA also added RTX Video Super Resolution as a ComfyUI-accessible pass, letting you chain a cheap, hardware-accelerated upscale after generation instead of loading a heavy neural upscaler into VRAM.

A realistic first-run checklist in prose form: verify your driver is current (Blackwell FP4 needs relatively recent drivers), confirm ComfyUI detects your GPU correctly in the startup log, start with a bundled template rather than a downloaded community workflow, generate one short clip at 480p or 720p to validate the pipeline, then scale resolution upward while watching the VRAM readout in the ComfyUI interface. If you exceed capacity, ComfyUI will either offload to system RAM (slow) or crash with an out-of-memory error — both are signals to drop resolution, shorten clip length, or switch to a smaller quantization.

VRAM Budgeting: Generation Plus Upscaling in One Workflow

Here's where many guides stop short, and where the real planning happens. A video model's VRAM footprint isn't just its weights. You need to budget for the latent space (which grows with frame count and resolution), attention computation buffers during denoising, the text encoder (CLIP/T5 variants can add 2–5 GB depending on whether they're quantized), and the VAE used to decode latents into pixels — video VAEs are notoriously memory-hungry because they process many frames at once, though tiled VAE decoding mitigates this heavily.

As a rough budget for a 720p, 81-frame generation with an FP4 video transformer: expect roughly 10–13 GB total on a well-configured setup, leaving a 16 GB card with a few gigabytes free. An FP16 version of the same model would push past 20 GB, exceeding most consumer cards entirely. Add a second-stage refinement or interpolation model and you're back at the ceiling. This is precisely why FP4 exists as a consumer feature — it converts an impossible workload into a feasible one.

For the upscaling stage specifically, you have three tiers of options. RTX Video Super Resolution is nearly free in VRAM terms since it runs as a lightweight hardware-assisted pass. Dedicated diffusion-based upscalers like SeedVR2 or similar video restoration models add another 6–15 GB depending on their own quantization — and yes, FP4/GGUF versions of upscalers exist too, following the same logic. Classical single-image enhancers applied per-frame sit in between. Plan your pipeline so the heaviest model loads, generates, unloads, and then the upscaler loads — ComfyUI handles this sequencing if your workflow is structured correctly, but a poorly ordered graph will try to hold everything in memory simultaneously and fail.

Common Mistakes That Waste VRAM and Cause Failures

The most frequent error is ignoring the text encoder. People quantize the main transformer to FP4, watch the weights shrink, then wonder why they still hit out-of-memory errors — because an unquantized T5 encoder is sitting in VRAM consuming several gigabytes. Quantize or offload the encoders too; most NVIDIA templates do this by default, but hand-assembled workflows often don't.

Second is frame count greediness. Doubling frames doesn't double latent memory linearly in all architectures — temporal attention can scale worse than linearly — so going from 49 to 121 frames can jump memory far more than expected. Start short, measure, then extend. Third is mixing precision sources blindly: loading an FP4 checkpoint through a loader node configured for FP16, or stacking multiple quantization schemes, produces errors or silent quality loss. Match the loader to the checkpoint format.

Fourth is skipping tiled decode. The standard VAE decode node tries to process entire clips at once and is the single most common OOM culprit in video workflows. Switching to tiled VAE decode costs a little speed and occasionally introduces faint tile seams, but saves enormous memory. Fifth, on the upscaling side, people assume more VRAM always equals better results — but an over-aggressive 4K upscale of a soft 480p generation just magnifies flaws. Generate at the highest resolution your VRAM allows, then upscale modestly; a clean 720p-to-4K pass beats a degraded 1080p attempt that crashed halfway through.

Alternatives and When FP4 Isn't the Right Choice

FP4 isn't the only memory-reduction path, and in some cases it isn't the best one. GGUF quantization (Q4_K_M, Q5, Q8 variants popularized by the llama.cpp ecosystem and ported to ComfyUI) works on any GPU brand including AMD and older NVIDIA cards, offers finer control over the quality-size curve, and has a large community producing quantized checkpoints. Its downside is speed — GGUF dequantization happens outside TensorRT's optimized FP4 kernels, so it's slower on RTX hardware than native FP4.

FP8 is the middle ground: half the memory of FP16 with almost no perceptible quality loss, fully supported on Ada and Blackwell, and often the better choice if your card has enough VRAM that you don't strictly need 4-bit compression. Cloud generation is the third alternative — renting an A100 or H100 for a few dollars per hour removes all local constraints, and makes sense if you generate daily at high volume, though it conflicts with the privacy and recurring-cost preferences that drive most local-generation users. Finally, NVIDIA's DGX Spark-class desktop systems occupy a niche for enthusiasts who want datacenter-adjacent memory capacity in a desk-side box, but at a price point far beyond typical consumer budgets.

## Cost Considerations and Total Investment Picture

The software stack itself — ComfyUI, the NVIDIA node pack, TensorRT, and the open-weight FP4 model checkpoints — is free. Your costs are hardware and electricity. On the low end, an RTX 5060 Ti 16 GB at around $429 delivers a genuinely usable FP4 video experience and is arguably the best value entry point in 2026. The RTX 5070 Ti at $799 adds comfortable headroom for simultaneous upscaling. The RTX 5090 at $1,999+ (street prices have often exceeded MSRP) is the enthusiast ceiling, handling 1080p-plus generation and heavy multi-model pipelines without breaking a sweat.

Don't forget supporting costs: a quality 850W PSU for higher-tier cards, adequate case cooling since video generation sustains 100% GPU load for minutes at a time, and 32–64 GB of system RAM to enable graceful offloading. Electricity is nontrivial for heavy users — a 450W-drawing GPU generating for two hours daily costs roughly $15–30 per month depending on regional rates, which over a year approaches the price of a mid-tier cloud subscription, though you own the hardware afterward.

When to Act and How to Decide

If you already own a 16 GB-or-better RTX 40- or 50-series card, there's no reason to wait — the toolchain is mature as of mid-2026, templates are stable, and the FP4 checkpoint ecosystem covers the major open video models. Budget an evening for setup and experimentation. If you own an 8 GB card, try it anyway with conservative settings, but temper expectations: you'll likely produce better results generating at 480p and investing effort in the upscaling stage than fighting for marginal 720p generations.

If you're buying hardware specifically for local AI video generation plus 4K upscaling, prioritize VRAM over raw shader speed — a 16 GB card will serve you longer than a faster 12 GB one, because model sizes keep growing and the difference between fitting a workflow and not fitting it is binary. Wait for sales rather than buying at launch premiums; GPU pricing in 2026 has normalized considerably from the shortage years. And whatever you choose, structure your workflow around the generate-then-upscale sequence from day one — it's the pattern that makes consumer-grade hardware deliver results that look professional at 4K.